Graph Transformation

19th International Conference, ICGT 2026, Held as Part of STAF 2026, Rennes, France, July 1–2, 2026, Proceedings

AvBlair Archibald,Oszkár Semeráth

Häftad, Engelska, 2026

Del 16624 i serien Lecture Notes in Computer Science

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Beskrivning

This book constitutes the refereed proceedings of the 19th International Conference on Graph Transformation, ICGT 2026, held in Rennes, France, during July 1–2, 2026.The 9 full papers and 4 short papers included in this book were carefully reviewed and selected from 19 submissions. The topics of the accepted papers cover a wide spectrum including new approaches to hypergraphs, improvements to the understanding of conflict analysis and parallel transformations, verification of graph transformation and of programs using graphs, and advancements in graphs for neural applications and stochastic rewriting.

Produktinformation

Utgivningsdatum:2026-06-28
Mått:155 x 235 x 16 mm
Vikt:384 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:235
Förlag:Springer Nature Switzerland AG
ISBN:9783032297297

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Innehållsförteckning

.- Technical Papers..- LR-Based Parsing of Hypergraph Languages: a Positional Grammar Approach..- Conditional Borrowing Hyperedge Replacement..- Higher-order Graph Transformation Utilizing Diagram Categories..- Parallel Transformations as Colimits..- Conflict Essences for Transformation Rules with Nested Application Conditions..- Formalising and Verifying Graph Programs with Higher-Order Logic..- Approximately Compatible Graph Predicates: Verifying Program Termination using Graph Grammars..- CGACell: A Cellular Automata-Based Graph Transformation Framework for Neural Pooling..- From Graph Rewriting to Markov Automata: Mass-Action Semantics for Stochastic and Probabilistic Systems..- Tool and Vision Papers..- GHL: An Extensible Library for Flexible and Performant Graph Pattern Matching and Rewriting..- EvolveGDB: Model-Driven Graph Schema Transformation..- User-defined Operations in GROOVE..- Benchmark First: Defining Tasks for Graph Transformation Learning.

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